Driving under drowsy conditions significantly escalates the risk of vehicular accidents. Although recent efforts have focused on using electroencephalography to detect drowsiness, helping prevent accidents caused by driving in such states, seamless human-machine interaction in driving scenarios requires a more versatile EEG-based system. This system should be capable of understanding a driver's intention while demonstrating resilience to artifacts induced by sudden movements. This paper pioneers a novel research direction in BCI-assisted driving, studying the neural patterns related to driving intentions and presenting a novel method for driving intention prediction. In particular, our preliminary analysis of the EEG signal using independent component analysis suggests a close relation between the intention of driving maneuvers and the neural activities in central-frontal and parietal areas. Power spectral density analysis at a group level also reveals a notable distinction among various driving intentions in the frequency domain. To exploit these brain dynamics, we propose a novel Masked EEG Modeling framework for predicting human driving intentions, including the intention for left turning, right turning, and straight proceeding. Extensive experiments, encompassing comprehensive quantitative and qualitative assessments on public dataset, demonstrate the proposed method is proficient in predicting driving intentions across various vigilance states. Specifically, our model attains an accuracy of 85.19% when predicting driving intentions for drowsy subjects, which shows its promising potential for mitigating traffic accidents related to drowsy driving. Notably, our method maintains over 75% accuracy when more than half of the channels are missing or corrupted, underscoring its adaptability in real-life driving.
Pre-Training Graph Contrastive Masked Autoencoders are Strong Distillers for EEG
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA（电气与计算机工程系，莱维大学）；Department of Bioengineering, Lehigh University, Bethlehem, PA, USA（生物工程系，莱维大学）；Center for Neuroscience Research, Children’s National Hospital, Washington, DC, USA（神经科学研究中心，儿童国家医院）；Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA（计算机科学与工程系，莱维大学）
Enhancing Interpretability of AR-SSVEP-Based Motor Intention Recognition via CNN-BiLSTM and SHAP Analysis on EEG Data
School of Control Science（控制科学学院）；Engineering Shandong University Jinan, Shandong（山东大学工程学院济南山东）；Shandong Inspur Science Research Institute Co., Ltd. Jinan, Shandong（山东 Inspur 科研院有限公司济南山东）
Mind2Drive: Predicting Driver Intentions from EEG in Real-world On-Road Driving
Department of Computer Science, Imam Mohammad Ibn Saud Islamic University（伊玛姆·莫哈默德·伊本·沙特伊斯兰大学计算机科学系）；Department of Computer Science, Princess Nourah bint Abdulrahman University（努拉·本·阿卜杜勒拉赫曼大学计算机科学系）
AADNet: Exploring EEG Spatiotemporal Information for Fast and Accurate Orientation and Timbre Detection of Auditory Attention Based on A Cue-Masked Paradigm
West China Hospital, Sichuan University（四川大学华西医院）；Institute of Electronic Engineering, China Academy of Engineering Physics（中国工程物理研究院电子工程研究所）
Enhancing Robustness of Asynchronous EEG-Based Movement Prediction using Classifier Ensembles
Robotics Innovation Center（机器人创新中心）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Institute of Medical Technology Systems（医疗技术系统研究所）；University of Duisburg-Essen（杜伊斯堡- Essen大学）
EEG Foundation Models: A Critical Review of Current Progress and Future Directions
Department of Computer Science & Engineering, University of Minnesota Twin Cities（计算机科学与工程系，明尼苏达大学双城分校）；Department of Bioengineering, University of Illinois at Urbana-Champaign（生物工程系，伊利诺伊大学厄巴纳-香槟分校）；Department of Neurology, Mayo Clinic（神经病学系，梅奥诊所）；Department of Neurology, University of Minnesota Twin Cities（神经病学系，明尼苏达大学双城分校）
NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
Korea University（高丽大学）；University of Cambridge（剑桥大学）；Stanford University School of Medicine（斯坦福大学医学院）；VA Palo Alto Health Care System（帕洛阿尔托退伍军人事务医疗系统）；Korea University College of Medicine（高丽大学医学院）
NOIR 2.0: Neural Signal Operated Intelligent Robots for Everyday Activities
Institute for Computational and Mathematical Engineering（计算与数学工程研究所）；Department of Computer Science（计算机科学系）；Department of Mechanical Engineering（机械工程系）
